NoSQL is often used to deal with large scale data needs, but most NoSQL solutions lack strong data integrity features. I will describe how one company is solving their big data problems using MapR M7. MapR's Data Platform (MDP) provides tables as a built in feature of the file system and exposes an HBase compatible API for accessing these tables. The architecture of the system is designed to scale to very large sizes, to avoid long pauses due to compaction and allow complete integration with MapR's snapshots and mirrors. We will talk about a specific customer use-case where the customer's priorities for Reliability, Ease of Use and Business Continuity made M7 the best choice.

1. Case Study of BigData use with MapR
M7 in the Enterprise Datacenter
Zeljko Dodlek
Sales Director DACH
zdodlek@maprtech.com
+49 (0) 151 120 555 07
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2. Agenda
 Ancestry Case Study
 MapR Overview
 Q&A
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3. Ancestry use Case (page 1)
 What does Ancestry do?
Ancestry.com is an online family history service that uses machine
learning and several other statistical techniques to provide services
such as ancestry information and DNA sequencing to its users.
 Business Challenges?
10 Billion records in a 4 PB DataStore
40.000 Record collections (date of birth/death, census, military
status,….)
2+ Million subscribers
10+ Million registered users
DNA matching added to their offering
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4. Ancestry use Case (page 2)
 Why MapR ?
HA Requirements for the NameNode & TaskTracker
Easy way to ingest Data into the cluster
Safe way for using different Jobs on the same cluster
Unified File & Table platform
Configuration
3 separate clusters
* DNA Matching
* Machine Learning
* Data Mining
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5. MapRTech Overview
 Enterprise Grade Hadoop Distribution
 Innovations in the areas of the DataPlatform, Map&Reduce
and HBase
 Enabling Customers to depend on our Hadoop Distribution
– No Single Points of Failure
– Guaranteeing SLA’s
– Easy to Install/run/expand
 Professional Services – Installation, consulting and training
 Support 7 x24
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6. MapR Distribution
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7. MapR’s value addition
Distribution made for the enterprise
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8. Expanding Hadoop Use Cases
Hadoop APIs
for Hadoop
Applications
ODBC and JDBC for
NFS for file-based
SQL-based
applications
applications
Mission
Real-time critical and SLA
Applications dependent
Applications
Blue = MapR Innovations
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9. No NameNode Architecture
Other Distributions (HDFS Federation) MapR
NAS
APPLIANCE
A B C D E F
NameNode
NameNode NameNode NameNode
E
DataNode DataNode DataNode
A F C D E D
DataNode DataNode DataNode
A B B C E B
DataNode DataNode DataNode
A D C F B F
 Multiple single points of failure  HA w/ automatic failover and re-replication
 Limited to 50M files per NameNode  Up to 1T files (> 5000x advantage)
 Performance bottleneck  Higher performance
 Commercial NAS required  100% commodity hardware
 Metadata must fit in memory  Metadata is persisted to disk
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10. Simplifying HBase Architecture
HBase
JVM
DFS HBase
JVM JVM
ext3 MapR Unified
Disks Disks Disks
Other Distributions
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11. Selected MapR Customers
 Global threat
analytics
 Intrusion detection & prevention  Recommendation Engine  Virus analysis
 Forensic analysis  Family tree connections
Major Credit Card Company  Clickstream Analysis
 Log analysis  Quality profiling/field
 Recommendation Engine  HBase failure analysis
 Fraud detection and Prevention
 Fraud  Customer
Detection Sentiment
 Channel  Advertising exchange  Network Analytics
analytics analysis and optimization
 Customer Revenue
Analytics
 Customer targeting  Monitors and measures
 ETL Offload
 Social media analysis behavior of online shoppers
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12. Thank You
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